Prenatal exposure to diabetes or cigarette smoke and postnatal sensitivity to cigarette smoke v1
Bibliographic record
Abstract
Prenatal exposure to maternal diabetes and cigarette smoke have been linked with negative respiratory consequences for offspring, including increasing risk for asthma and chronic obstructive pulmonary disease (COPD). This project aims to explore how prenatal factors influence offspring sensitivity to cigarette smoke later in life, as a surrogate for COPD risk. Downstream indicators of lung function, inflammation, and tissue for molecular analysis were collected. All the procedures for this study were done in accordance with University of Manitoba Animal Ethics (#21-011). In keeping with the 3R's of animal research, a single control group was used in this study, exposures and data were collected at the same time as the experimental groups. In this way, the control mice act as a relevant comparator for multiple prenatal exposures without necessitating separate control groups. There are four prenatal exposure groups generated from this protocol: Control (low-fat diet and room air), Maternal Diabetes (high-fat diet and room air), Maternal Smoking (low-fat diet and cigarette smoke), and Maternal Diabetes with Smoking (high-fat diet and cigarette smoke). The two aims of this project were to address 1) whether individual prenatal factors influence offspring sensitivity to cigarette smoke, 2) if prenatal exposure interact to influence influence cigarette smoke sensitivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".